 Okay, welcome back, everyone, with CUBE coverage here at AWS Remars 2022. I'm John Furrier, host of theCUBE. Remars, part of the three big re-events, re-invent is the big one, reinforces security. Remars is the confluence of industrial space, automation, robotics, and machine learning. But a great guest here, Mohamed Ciel, senior consultant, solutions architect at CAP. Jim and I welcome to theCUBE. Thanks for coming on. Thank you. So you just were hearing the questions we had with the professor from Okta ML, Professor Washington. So he's in the weeds on machine learning. He's down getting dirty with all the hardcore, uncoupling it from the hardware. Machine learning has gone really supernova in the past couple of years. And this show points to the tipping point where machine learning is driving space, driving robotics, industrial edge, at unprecedented rates. So it's kind of moving from the old, I won't say old, a couple of years ago, in legacy AI. I mean old school AI is kind of the same new school with a twist. It's just modernized and as fast or cheap or smaller chips. Yeah, I mean, but there is a change also in the way it's working. So you had the classical AI where you are detecting something and then you're making an action. You are perceiving something, making an action. You're detecting something and you're assuming something that has been perceived. But now we are moving towards more deeper learning. So AI where you have to train your model to do things or to detect things and hope that it will work. And there's like, of course, a lot of research going on into explainable AI to help facilitate that. But that's where the challenges come into play. Well, Mama, first let's take, what do you do over there? Talk about your role specifically. You do a lot of things architecting around AI, machine learning. What's your role? Yeah, so we basically are working in automotive to help OEMs and tier one suppliers validate ADAS functions that they are working on. So advanced driving assistance systems. There are many levels that are, when we talk about it, so it can be something simple like blind spot detection, just a warning function, and it goes all the way, so S-A-E, so. So there's like the easy stuff and then the hard stuff. Exactly, yeah. That's what you're just getting at. Yeah, and the easy stuff, you can test validate quite easily because if you get it wrong, the impact is not that high. The complicated stuff, if you have it wrong, then that can be very dangerous. Well, I got to say, the automotive one was there so fascinating because it's been so archaic and just in the past recent years, and Tesla's the poster child for this. You see that, you go, oh my God, I love that car. I want to have this software driven car, and it's amazing, and I don't get a Tesla now because it's more like, I should have gotten it earlier. Now I'm going to just hold my ground. Everyone has it. Everyone's got it in Palo Alto. I'm not going to get another car. No way, but you're starting to see a lot of the other manufacturers just in the past five years, they're leveling up. It may not be as cool and sexy as the Tesla, but they're there, and so what are they dealing with when they talk about data and AI? What's some of the challenges that you're seeing that they're grappling with in terms of getting things integrated, developing pipelines, R&D, they wrangle in data, take us through some of those things. I mean, like when I think about the challenges that autonomous or the automakers are facing, I can think of three big ones. So first is the amount of data they need to do their training and more importantly, the validation. So we are talking about petabytes or hundred of petabytes of data that has to be analyzed, validated, annotated, so labeling to ground truth, processed, reprocessed many times with every iteration of a new software. So that is a lot of data, a lot of computational power, and you need to ensure that all of the processing, all of handling of the data allows you complete transparency of what is happening to the data, as well as complete traceability. So your, for homologation, so approval process for these functions so that they can be released in cars that can be used on public roads. You need to have traceability, like you are supposed to be able to reproduce the data to validate your work that was done so you can prove that your function is successful or working as expected. So this, the big data is the first challenge I see that all the automotive makers are tackling. The second big one I see is understanding how much testing is enough. So with AI or with classical approach, you have certain requirements, how a function is supposed to work. You can test that with some test cases based on your architecture and you have a successful or failed result. With deep learning, it gets more complicated. What are they doing with deep learning? Give an example. I mean, so you are, you need to then start thinking about statistics that I will test enough data with like a failure rate of potentially like 0.001%. How much data do I need to test to make sure that I am achieving that rate? So then we are talking about in terms of statistics which requires a lot of data because the failure rate that we want to have is so low. And it's not only like failure in terms of that something is always detected and if it's there, but it's also having like a low false positive rate. So you are only detecting objects which are there and not like, momentum objects. What's some of the trends you're seeing across the client base in terms of the patterns that they're all kind of, what's the state of their mindset and position with AI and some of the work they're doing? Are they feeling, do you feel like they're all crossed over, crossed the chasm, so to speak, in terms of executing? Are they still in experimental mode and driving the full capabilities? Is it conservative or is it progressive? I mean, it's a mixture of both. So I am in German automotive where I'm from, there is four functions which are more complicated ones. There's definitely hesitancy to release them too early in the car unless we are sure that they are safe. But of course, for functions which are assisting the drivers, everyday usage, they are widely available. Like one of the things, like when we talk about this complex function. Highly available or available? I would say highly available. Higher availability and highly available. Okay. Yeah, there's a distinction. Yeah, I mean, I bring up as a joke, a Jedi contract. I mean, in our architecture, so when we are developing our solution, high availability is one of our requirements and it is high available. But the ADAS functions are now available in more and more cars. Latency, madam. It's kind of a joke of storage. It's a storage joke. You know, it's latency, you got that, okay. But these are decisions that have to be made. I mean, they are still being made. So I mean, we haven't reached like level five, which is the highest level of autonomous driving yet on public roads. That's hard, that's hard to do. Yeah. And I mean, the biggest difference, like as you go above these levels is in terms of availability. So are these functions, can they handle all possible scenarios? Are they only available in certain scenarios? And of course, the responsibility. So in the end, so with Tesla, you would be like, if you had one, you would be the person who is in control or responsible to monitor it. But as we go- Actually, the reason I don't have a Tesla, all my family would want one. I don't want to get it on a Tesla. Yeah, but I mean, but that's the liability is currently on you, if like you're not monitoring it. So talk about AWS, the relationship that Capgemin has with AWS. Obviously the partnerships there, you're here. And this show is really a commitment to, this is a future to me, this is just like, this is the future. And this is it all right here. Industrial innovation is going to come massive, back office cloud done deal, data centers, hybrid, somewhat multi-cloud, I guess, but hybrid is a steady state in the back office cloud, game over. Amazon, Azure, Google, Alibaba done. Super clouds underneath great. This is a digital transformation in the industrial area. Yeah. This is the big thing. What's your relationship with AWS? So as I mentioned, the first challenge data, like we have so much data, so much computational power, and it's not something that is always needed. You need it like on demand. And this is where like a hyperscaler cloud provider like AWS can be the key to achieve like the high, the acceleration that we are providing to our customers using our technology built on top of AWS services. We did a breakout session this during remarse where we demonstrated a couple of small tools that we have developed out of our offering. One of them was ability to stream data from the vehicle that is collecting data worldwide. So during the demo we did it from Vegas, driving on the strip as well as from Germany. And while this data is uploaded, it's at the same time real-time anonymized to make sure we are compliant with the data privacy, security. Of course, yeah. That's hard to do right there. And so the faces are blurred, the licenses are blurred. We also then at the same time can run object detection. So we have real-time monitoring of what our feed is doing worldwide and. I'm just curious. Do you do that blurring? Is that part of a managed service you call an API or is that built into the code? From part of our DSV we have many different service offerings, so data production, data test strategy orchestration. So part of data production is worldwide data collection and we can then also offer data management services which include then anonymization, data quality check. And that's as soon as you provide to the customer. Okay, got it. So of course in collaboration with the customer. So our platform is very modular, microservices based. The idea being if the customer already has a good ML model for anonymization, we can plug it into our platform running on AWS. If they want to use it, we can develop one or we can use one of our existing ones or something off the shelf or like any other supplier can provide one as well. And we all integrate. So you're tight with Amazon Web Services in terms of your cloud, your service. It's a cloud. It's a Capgemini SuperCloud basically. Okay, so we call it SuperCloud. We made that a thing and reinvent. Charles Fitzgerald would disagree, but we will debate him. It's a SuperCloud. But okay, you got your SuperCloud. What's the coolest thing that you think you're doing right now that people should pay attention to? I mean the cool thing that we are currently working on. So from the keynote today, we talked about also synthetic data for validation. So I mean, we are working on digital twin creation. So we are capturing data in real world, creating a virtual identity of it. And that allows you the freedom to create multiple scenarios out of it. So that's also something where we are using machine learning to determine what are the parameters you need to change between, or so you have one scenario such as a cut in scenario. And you can change a cut in scenario for someone that's cutting in front of you or an overtake scenario. And so I mean, in real world, someone will do it in probably a nicer way. But of course, it is possible at some point. Cognition to the cars. It comes in a vehicle. I mean, at some point someone would be very aggressive with it, we might not record it. You might be able to predict too. I mean the predictions, you can say this guy's weaving, he's a potential candidate. It is possible, yes. But to ensure that we are testing these scenarios, we can translate a real world scenario into a digital world, change the parameters so the distance between those two is different and use machine learning to change these parameters. So this is exciting. And the other thing we are excited. That is pretty cool. I would admit that's very cool. The other thing we are trying to do is reduce the cost for the customer in the end. So we are collecting petabytes of data. Every time they make updates to the software, they have to resimulate it or replay this data. So that they can- Petabytes? Petabytes of data. And sometimes on a physical hardware and loop device. And then this re- That's called a really heavy edge. You don't want to be moving that around the Amazon cloud. That's the challenge. And once we have replayed this or resimulated it, we still have to calculate the KPIs out of it. And what we are trying to do is optimize this test orchestration so that we are minimizing the simulation so you don't want the data to be going to the edge unnecessarily. And once we get this data back to optimize the way we are doing the calculation. So you're not calculating- It's huge data integrity management, new kind of thing going on here. It's kind of, is it new or is it- I mean it's- Sounds new to me. The scale is new. So the management of the data, having that whole traceability that has been in automotive. So also kept geminized and involved in aerospace. So in aerospace, having this kind of high, this validation be very strictly monitored is normal. But now we have to think about how to do it on this large scale. And that's what, I think that's the biggest challenge and hopefully what we are trying to solve with our DESV offering. All right, Mohammed, thanks for coming on the queue. I really appreciate it. Great way to close out, re-marge our last interview with the show. Thanks for coming on. I appreciate your time. I mean like just one last comment. Like so, I think in automotive, like part of the automation, the future is quite exciting. And I think that's where we have to be hopeful that- Well, the show is all about hope. I mean you had space, moon habitat, you had climate change, potential solutions. You have new functionality that we've been waiting for. And I've watched every episode of Star Trek and Skynet, kind of Skynet going on air. Robots. Robots running cubes. Robots pose someday. You never know. Thanks for coming on. Appreciate it. Okay, that's theCUBE here, wrapping up, re-marge. I'm John Furrier, you're watching theCUBE. Stay with us for the next event, next time. Thanks for watching.